基于隶属度条件放宽的模糊聚类目标检测算法  

Fuzzy Clustering Target Detection Algorithm Based on Membership Degree Condition Relaxation

作　　者：钱司远 沈雅婷 田瑞淼 王正东 顾宇航 汤风铃 QIAN Siyuan;SHEN Yating;TIAN Ruimiao;WANG Zhengdong;GU Yuhang;TANG Fengling(Nanjing University of Science and Technology Zijin College,Nanjing 210023,China)

机构地区：[1]南京理工大学紫金学院,江苏南京210023

出　　处：《现代信息科技》2025年第6期33-38,45,共7页Modern Information Technology

基　　金：江苏省高校“青蓝计划”资助;2024江苏省创新训练通用资助项目(202413654018Y);2023南京理工大学紫金学院大学生创新训练计划资助项目(ZJSRTP2023118)。

摘　　要：文章的研究对象是模糊C均值(FCM)聚类算法,旨在提升FCM在处理复杂数据时的有效性。传统FCM假设一个样本对每个聚类的隶属度之和为1,称为样本贡献度。然而,在处理噪声点和孤立点时,聚类的有效性可能会下降。为此,文章提出了一种新型模糊聚类方法,通过放宽隶属度之和为1的条件,引入新的隶属度划分方式,即隶属度之和为样本总数n的创新定义。该方法根据样本与聚类中心的距离动态调整隶属度,并将隶属度划分为三个层次,以提升聚类的有效性。通过数学推导和实验验证,改进后的算法在处理复杂数据集时,准确率提高了6.41%,召回率提升了5.81%,显著提升了算法的性能。The research object of this paper is the Fuzzy C-Means(FCM)clustering algorithm,which aims to improve the effectiveness of FCM in dealing with complex data.Traditional FCM assumes that the sum of the membership degree of a sample to each cluster is 1,which is called sample contribution degree.However,when dealing with noise points and outliers,the effectiveness of clustering may decrease.To this end,this paper proposes a new fuzzy clustering method.By relaxing the condition that the sum of membership degree is 1,a new membership degree division method is introduced,that is,the sum of membership degree is an innovative definition of the total number of samples n.This method dynamically adjusts the membership degree according to the distance between the sample and the clustering center,and divides the membership degree into three levels to improve the effectiveness of clustering.Through mathematical derivation and experimental verification,the improved algorithm improves the accuracy by 6.41%and the recall rate by 5.81%when dealing with complex data sets.It significantly improves the performance of the algorithm.

关 键 词：模糊C均值 隶属度 样本贡献度 目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]